ARTIFICIAL INTELLIGENCE FOR

PREDICTION OF SEPSIS IN VERY LOW

BIRTH WEIGHT INFANTS

Markus Leskinen MD PhD, Neonatologist

Children’s Hospital, University of Helsinki and Helsinki University

Hospital

The sepsis case in a nutshell

It’s all about

saving babieswith data and

artificial intelligence

T h e i s s u e

Sepsis is common

problem in NICUs with

severe complications.

Detection is difficult:

Unspecific and gradual

signs.

W h a t w e d i d

Used available

clinical data and

advanced analytical

methods to identify

upcoming sepsis risk.

T h e o u t c o m e

The model is able to

identify sepsis risk 24

hours before a

clinician with high and

clinically significant

accuracy.

Neonatal intensive care unit (NICU),

Children’s Hospital, Helsinki

• Tertiary hospital serving Southern Finland

with population of 1,6 million

• 17 000 deliveries per year

• 29 patient beds, 16 intensive care beds

• More than 1600 patients per year

• 120-150 VLBW infants (BW <1500g) per year

• Centricity Critical Care (GE Healthcare)

patient monitoring system from 1999

• > 12 000 patients

• > 2000 VLBW infants

NICU and Data Generation

Patient monitor

Ventilator

Infusion pumps

aEEG

monitor

iNO

delivery

system

Data Streams in NICU

• Monitoring of vital functions

• ECG 240 Hz

• 20,7 million measurements per day

• Invasive blood pressure measurement 120 Hz

• Oxygen saturation 60 Hz

• Temperature

• Respiratory rate

• Transcutaneous pO2, pCO2

• Direct connection of ventilators and other medical equipment

• Laboratory data

• Manually registered data

• Data measured by staff

• Drug prescription

• Doctors’ and nurses’ records

Centralized information system

• Data collection

• Analysis, visualization

• Storage

• Our NICU database• 2099 VLBW infants 1999-2013

• Median gestational age 28+6 weeks,

median birth weight 1100 g

Sepsis in Newbown

• Generalized infection with bacteremia

• Early onset sepsis

• <72 h of age

• Pathogens from mother, usually GBS

• Late onset sepsis, mostly VLBW infants• >72 h of age

• Usually hospital acquired

• 12% of VLBW infants develop late sepsis during NICU stay

• Sepsis is associated with high risk of mortality and long-

term neurodevelopmental sequelae

Diagnostic challenges

• Unspecific, gradual signs• feeding problems, fatigue

• tachypnea, apneic spells, tachy- or bradycardia

• No pathognomonic lab test

• CRP, late response

• White blood cell count: leukopenia, leukocytosis

• Blood glucose, metabolic acidosis

• Blood culture – gold standard

• slow, invasive, false negatives

Current management of suspected sepsis

• Blood culture and (prophylactic) antibiotic therapy for high

risk VLBW patients with signs on suspected sepsis

• duration and drug choice based on result of blood culture and clinical

situation

• Overuse of antibiotics

• disturbed intestinal microbiome

• resistence to antibiotics

• Potential delay in antibiotic therapy because of unspecific

signs

• increased morbidity and mortality

Can artificial intelligence be used for early

diagnosis of sepsis in VLBW infants?

• Predictive machine learning models are able to detect events and

abnormalities b e f o r e n o t a b l e p a t h o l o g i c a l s y m p t o m s can

be observed by conventional means.

• Our goal was to develop a computational model for p r e d i c t i n g

n e o n a t a l s e p s i s using routinely collected patient monitoring data,

laboratory results and patient record information.

• 173 VLBW infants with proven late onset sepsis

• positive blood culture and clinical diagnosis

• Control group 1702 VLBW infants without sepsis

• 106 VLBW infants with clinical suspicion of sepsis, but with

negative blood culture

• Time zero = blood culture

• Analysis of collected data 48 h prior to blood culture for

patterns that could identify sepsis with maximal accuracy

24 h prior to blood culture

• Monitor data stored as 2 min means

• Calculations using IBM Watson

• CHAID decission tree algorithm

Retrospective sepsis analysis

Measured parameters

• Monitor data

• heart rate, respiratory rate, blood pressure, oxygen saturation,

temperature, supplemental oxygen

• 2 min averages of 10 s medians

• Manual measurements

• gestational age, sex, birth weight, actual weight, diuresis

• Lab

• blood culture, blood glucose, electrolytes, CRP, blood cell count,

blood gas analyses

Derived parameters

• Variation in heart rate and temperature during last 10 min and 1 h

• Variation in respiratory rate during last 10 min

• Min ja Max temperature, pH, base excess during last 12 h

• Diuresis (ml/h/kg) during last 12 h

• Episodes of hypoxia during last 12 h

• Oxygen saturation/need for additional oxygen

• Change in mean saturation during last 3 h

• Cumulative time of hypoxia / total time of treatment

• Percent time in of hypoxia during last 3 h

• Ratio and distribution of systolic and diastolic blood pressure

Sensitivity and specificity 24 h prior to

blood culture

• At 24 h the prediction model identified blood positive

sepsis with 82% sensitivity and ja 96% specificity

• Positive predictive value 0.88

• Negative predictive value 0.94

Main parameters used by the prediction

model1. Percentage of time at low oxygen saturation / 3h

2. Arterial PO2

3. Lowest capillary pH / 12h

4. Change in mean saturation during last 3 h

5. Capillary pH

6. Oxygen saturation /need for additional oxygen

7. Capillary PO2

8. Arterial BE

9. White blood cell count

10.Capillary PCO2

Timeline of sepsis risk score

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

48 44 40 36 32 28 24 21 17 13 9 5 1

Time (h) before blood culture

Patients with sepsis

Controls

Conclusions and future development

• Our algorithm can be identify sepsis in VLBW infants 24 h

earlier than regular clinical methods

• Next step is real time analysis of risk score of sepsis in

VLBW infants

• Web-based tool for clinicians

• Can other clinical complications in NICU be detected by

machine learning?

• Necrotising enterocolitis (NEC)

• Retinopathy of prematurity (ROP)

• Intraventricular hemorrhage (IVH)

Collaborators

• HUS Lastenklinikka

• Sture Andersson

• Markus Leskinen

• IBM

• Antti Heino

• Viljami Venekoski

• Mikko Laakko

• Maija Väisänen

• Laura Sutinen

Cohort, n = 2091

Sepsis positive blood culture

n = 269

No sepsis positive blood culture

n = 1822

No positive blood culture for

candida albicans, candida

parapsilosis or yeast

n = 182

No sepsis within first 72 hours of

admission

n = 175

Clinical sepsis diagnosis

n = 182

Admission time < 180 days

n = 173Admission time < 180 days

n = 1558

No clinical sepsis diagnosis

n = 1578

SEPSIS POSITIVE TARGET GROUP REFERENCE GROUP

More than 100 records per patient

n = 1517

No positive blood culture for

candida albicans, candida

parapsilosis or yeast

n = 1569

More than 100 records per patient

n = 173